Model Based Design Environment for Data-Driven Embedded Signal Processing Systems

被引:6
作者
Sudusinghe, Kishan [1 ]
Cho, Inkeun [1 ]
van der Schaar, Mihaela [2 ]
Bhattacharyya, Shuvra S. [1 ]
机构
[1] Univ Maryland, College Pk, MD 20742 USA
[2] Univ Calif Los Angeles, Los Angeles, CA USA
来源
2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE | 2014年 / 29卷
关键词
Dataflow; Embedded Systems; DDDAS; Multi-mode; scheduling; energy-constrained; DATA-FLOW; SPEECH;
D O I
10.1016/j.procs.2014.05.107
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this paper, we investigate new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource-and energy-constrained embedded environments, such as UAVs, mobile communication platforms and wireless sensor networks. Signal processing applications, such as keyword matching, speaker identification, and face recognition, are of great importance in such environments. Due to critical application constraints on energy consumption, real-time performance, computational resources, and core application accuracy, the design spaces for such applications are highly complex. Thus, conventional static methods for configuring and executing such embedded DSP systems are severely limited in the degree to which processing tasks can adapt to current operating conditions and mission requirements. We address this limitation by developing a novel design framework for multi-mode, data driven signal processing systems, where different application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application.
引用
收藏
页码:1193 / 1202
页数:10
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